Remote sensing of crop structure and field condition enabled through smart sensors and satellite data

Vegetation canopy structure is fundamental to understanding plant-environment interactions, particularly in relation to energy, water, and carbon fluxes. Monitoring canopy structure parameters are crucial for advancing our understanding of canopy function and improving the accuracy of ecological and agricultural models. However, the monitoring of crop canopy structure and field conditions still face challenges in terms of instrument and methodology. The primary focus of this research is to address one key science question: How can we efficiently characterize crop canopy structure and field condition to support improved monitoring of crop productivity? In this dissertation, we investigated three key canopy structure parameters, including leaf area index (LAI), leaf angle distribution (LAD), and clumping index (CI), as well as evaluated land surface temperature (LST) to better understand crop canopy and field conditions. Specifically, Chapter 2 provides a comprehensive evaluation of several widely used indirect optical instruments, aiming to enhance the understanding and guide the correct application and interpretation of these instruments for estimating average leaf angle. Additionally, two classical methods for estimating average leaf angle were tested, and modifications to one of the methods were proposed for enhanced accuracy. Building on Chapter 2, Chapter 3 further refines the methodologies used for LAD estimation. We developed a novel and accurate three-step approach for LAD estimation, which is superior in reducing the number of known variables during the inversion process, compared with traditional approaches. We also evaluated the impact of non-leaf plant elements on the indirect instruments and demonstrated that their impact is minimal during early and peak growth stages but becomes more significant during the senescent stage. Chapter 4 presents a novel approach to estimating the CI of row crops using a 30°-tilted digital camera, adapting three classical CI retrieval methods. The influencing factors of clumping index, including segment size, view zenith angle and seasonal trajectories, were thoroughly investigated. Chapter 5 evaluates several satellite LST products for their applicability in agricultural monitoring in the U.S. Corn Belt. This dissertation contributes to the efficient quantification of crop canopy structure using low-cost camera sensors, offering a scalable solution for acquiring extensive ground truth data. These advancements will support cross-scale sensing and improve the modeling and monitoring of canopy structure and crop productivity.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Natural Res & Env Sciences

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Li, Kaiyuan

Contributors dc:contributor

• Guan, Kaiyu
• Bernacchi, Carl J.
• Peng, Bin
• Chen, Jingming
• Chen, Min

• Canopy structure monitoring
• land surface temperature

Statement dc:rights

• Copyright 2025 Kaiyuan Li

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129755

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129755